 I = imread('circuit.tif');
 imshow(I);
 corners = detectFASTFeatures(I,'MinContrast',0.1);
 J = insertMarker(I,corners,'circle');
 figure
 imshow(J);
/*Local features and their descriptors, which are a compact vector representations of a local neighborhood, 
are the building blocks of many computer vision algorithms. 
Their applications include image registration, object detection and classification, 
tracking, and motion estimation. 
Using local features enables these algorithms to better handle scale changes, rotation, and occlusion. 
The Computer Vision System Toolbox™ provides the FAST, Harris, 
and Shi & Tomasi methods for detecting corner features, and the SURF and MSER methods for detecting blob features.
The toolbox includes the SURF, FREAK, BRISK, and HOG descriptors. 
You can mix and match the detectors and the descriptors depending on the requirements of your application.

What Are Local Features?
Local features refer to a pattern or distinct structure found in an image, such as a point, edge, or small image patch. 
They are usually associated with an image patch that differs from its immediate surroundings by texture, color, or intensity.
What the feature actually represents does not matter,
just that it is distinct from its surroundings. Examples of local features are blobs, corners, and edge pixels.*/